%A Wang Xiufang,Sheng Shu,Lu Yan %T Analyzing Public Opinion from Microblog with Topic Clustering and Sentiment Intensity %0 Journal Article %D 2018 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2017.1107 %P 37-47 %V 2 %N 6 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4517.shtml} %8 2018-06-25 %X

[Objective] This paper builds a model to monitor the trending topics from microblogs, aiming to deal with the issues of text drifting and quantitation of sentimental polarity. [Methods] First, we proposed a public opinion analysis model based on topic clustering and sentiment intensity. Then, we used the time series regression analysis to predict the sentimental changes among the trending topics. [Results] The prediction accuracy of our model reached 88.97%, which was about 7% higher than the iLab-Edinburgh model. [Limitations] More research is needed to study the early warning mechanisms for emergency events. [Conclusions] The proposed model could improve the prediction accuracy of sentimental changes, which provides an effective way to analyze the public opinion from microblogs.